In the paper a soft sensor designed to estimate the acid gases hydrogen sulfide (H2S) in the tail stream of a sulphur recovery unit (SRU) in a refinery located in Sicily, Italy, is described. In particular a model stacking approach is proposed to improve the estimation performance of the soft sensor. Neural networks, principal component analysis (PCA) and partial least squares (PLS) approaches are used for the realization of the first level's models and Simple average, neural networks and PLS are used as combination approaches. The validity of the proposed aggregation strategies has been verified by a comparison with the performance of a neural model. The obtained soft sensor will be implemented in the refinery in order to replace the measurement device during maintenance and guarantee continuity in the monitoring and control of the plant
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